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@ARTICLE{Achlioptas2009Explosive,
  author = {Achlioptas, Dimitris and D'Souza, Raissa M. and Spencer, Joel},
  title = {{Explosive Percolation in Random Networks}},
  journal = {Science},
  year = {2009},
  volume = {323},
  pages = {1453--1455},
  number = {5920},
  month = mar,
  abstract = {{Networks in which the formation of connections is governed by a random
	process often undergo a percolation transition, wherein around a
	critical point, the addition of a small number of connections causes
	a sizable fraction of the network to suddenly become linked together.
	Typically such transitions are continuous, so that the percentage
	of the network linked together tends to zero right above the transition
	point. Whether percolation transitions could be discontinuous has
	been an open question. Here, we show that incorporating a limited
	amount of choice in the classic Erd\"{o}s-R\'{e}nyi network formation
	model causes its percolation transition to become discontinuous.}},
  citeulike-article-id = {4169858},
  citeulike-linkout-0 = {http://users.soe.ucsc.edu/\~{}optas/papers/explosive.pdf},
  citeulike-linkout-1 = {http://dx.doi.org/10.1126/science.1167782},
  citeulike-linkout-2 = {http://www.sciencemag.org/content/sci;323/5920/1453.abstract},
  citeulike-linkout-3 = {http://www.sciencemag.org/content/sci;323/5920/1453.full.pdf},
  citeulike-linkout-4 = {http://www.sciencemag.org/cgi/content/abstract/323/5920/1453},
  citeulike-linkout-5 = {http://view.ncbi.nlm.nih.gov/pubmed/19286548},
  citeulike-linkout-6 = {http://www.hubmed.org/display.cgi?uids=19286548},
  day = {13},
  doi = {10.1126/science.1167782},
  keywords = {percolation},
  pmid = {19286548},
  posted-at = {2012-03-25 15:23:28},
  priority = {2},
  url = {http://users.soe.ucsc.edu/\~{}optas/papers/explosive.pdf}
}

@ARTICLE{Adomavicius2005Toward,
  author = {Adomavicius, G. and Tuzhilin, A.},
  title = {{Toward the Next Generation of Recommender Systems: A Survey of the
	State-of-the-Art and Possible Extensions}},
  journal = {Knowledge and Data Engineering, IEEE Transactions on},
  year = {2005},
  volume = {17},
  pages = {734--749},
  number = {6},
  abstract = {{This paper presents an overview of the field of recommender systems
	and describes the current generation of recommendation methods that
	are usually classified into the following three main categories:
	content-based, collaborative, and hybrid recommendation approaches.
	This paper also describes various limitations of current recommendation
	methods and discusses possible extensions that can improve recommendation
	capabilities and make recommender systems applicable to an even broader
	range of applications. These extensions include, among others, an
	improvement of understanding of users and items, incorporation of
	the contextual information into the recommendation process, support
	for multcriteria ratings, and a provision of more flexible and less
	intrusive types of recommendations.}},
  citeulike-article-id = {171426},
  citeulike-linkout-0 = {http://ids.csom.umn.edu/faculty/gedas/papers/recommender-systems-survey-2005.pdf},
  citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1423975},
  posted-at = {2012-03-25 15:56:46},
  priority = {2},
  url = {http://ids.csom.umn.edu/faculty/gedas/papers/recommender-systems-survey-2005.pdf}
}

@ARTICLE{jeong00,
  author = {Albert, Reka and Jeong, Hawoong and Barabasi, Albert-Laszlo},
  title = {{Error and attack tolerance of complex networks}},
  journal = {Nature},
  year = {2000},
  volume = {406},
  pages = {378--382},
  number = {6794},
  month = jul,
  abstract = {{Many complex systems display a surprising degree of tolerance against
	errors. For example, relatively simple organisms grow, persist and
	reproduce despite drastic pharmaceutical or environmental interventions,
	an error tolerance attributed to the robustness of the underlying
	metabolic network. Complex communication networks display a surprising
	degree of robustness: although key components regularly malfunction,
	local failures rarely lead to the loss of the global information-carrying
	ability of the network. The stability of these and other complex
	systems is often attributed to the redundant wiring of the functional
	web defined by the systems' components. Here we demonstrate that
	error tolerance is not shared by all redundant systems: it is displayed
	only by a class of inhomogeneously wired networks, called scale-free
	networks, which include the World-Wide Web, the Internet, social
	networks and cells. We find that such networks display an unexpected
	degree of robustness, the ability of their nodes to communicate being
	unaffected even by unrealistically high failure rates. However, error
	tolerance comes at a high price in that these networks are extremely
	vulnerable to attacks (that is, to the selection and removal of a
	few nodes that play a vital role in maintaining the network's connectivity).
	Such error tolerance and attack vulnerability are generic properties
	of communication networks}},
  citeulike-article-id = {204822},
  citeulike-linkout-0 = {http://www.barabasilab.com/pubs/CCNR-ALB\_Publications/200007-27\_Nature-ErrorAttack/200007-27\_Nature-ErrorAttack.pdf},
  citeulike-linkout-1 = {http://dx.doi.org/10.1038/35019019},
  citeulike-linkout-2 = {http://dx.doi.org/10.1038/406378a0},
  citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/10935628},
  citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=10935628},
  day = {27},
  doi = {10.1038/35019019},
  issn = {1476-4687},
  pmid = {10935628},
  posted-at = {2012-03-25 15:22:50},
  priority = {2},
  url = {http://www.barabasilab.com/pubs/CCNR-ALB\_Publications/200007-27\_Nature-ErrorAttack/200007-27\_Nature-ErrorAttack.pdf}
}

@ARTICLE{Brede2005Assortative,
  author = {Brede, Markus and Sinha, Sitabhra},
  title = {{Assortative mixing by degree makes a network more unstable}},
  year = {2005},
  month = jul,
  abstract = {We investigate the role of degree correlation among nodes on the stability
	of complex networks, by studying spectral properties of randomly
	weighted matrices constructed from directed Erd\"{o}s-R\'enyi and
	scale-free random graph models. We focus on the behaviour of the
	largest real part of the eigenvalues, \$\lambda\_\text{max}\$, that
	governs the growth rate of perturbations about an equilibrium (and
	hence, determines stability). We find that assortative mixing by
	degree, where nodes with many links connect preferentially to other
	nodes with many links, reduces the stability of networks. In particular,
	for sparse scale-free networks with \$N\$ nodes, \$\lambda\_\text{max}\$
	scales as \$N^\alpha\$ for highly assortative networks, while for
	disassortative graphs, \$\lambda\_\text{max}\$ scales logarithmically
	with \$N\$. This difference may be a possible reason for the prevalence
	of disassortative networks in nature.},
  archiveprefix = {arXiv},
  citeulike-article-id = {270888},
  citeulike-linkout-0 = {http://arxiv.org/pdf/cond-mat/0507710v1},
  citeulike-linkout-1 = {http://arxiv.org/abs/cond-mat/0507710},
  citeulike-linkout-2 = {http://arxiv.org/pdf/cond-mat/0507710},
  day = {29},
  eprint = {cond-mat/0507710},
  posted-at = {2012-03-25 15:57:34},
  priority = {2},
  url = {http://arxiv.org/pdf/cond-mat/0507710v1}
}

@ARTICLE{chakrabarti2006graph,
  author = {Chakrabarti, D. and Faloutsos, C.},
  title = {{Graph mining: Laws, generators, and algorithms}},
  journal = {ACM Computing Surveys (CSUR)},
  year = {2006},
  volume = {38},
  pages = {2},
  number = {1},
  citeulike-article-id = {7736188},
  citeulike-linkout-0 = {http://www.cs.cmu.edu/\~{}deepay/mywww/papers/csur06.pdf},
  posted-at = {2012-03-25 15:58:27},
  priority = {2},
  publisher = {ACM},
  url = {http://www.cs.cmu.edu/\~{}deepay/mywww/papers/csur06.pdf}
}

@ARTICLE{nicholas10,
  author = {Christakis, Nicholas A. and Fowler, James H.},
  title = {{Social Network Sensors for Early Detection of Contagious Outbreaks}},
  journal = {PLoS ONE},
  year = {2010},
  volume = {5},
  pages = {e12948+},
  number = {9},
  month = sep,
  abstract = {{Current methods for the detection of contagious outbreaks give contemporaneous
	information about the course of an epidemic at best. It is known
	that individuals near the center of a social network are likely to
	be infected sooner during the course of an outbreak, on average,
	than those at the periphery. Unfortunately, mapping a whole network
	to identify central individuals who might be monitored for infection
	is typically very difficult. We propose an alternative strategy that
	does not require ascertainment of global network structure, namely,
	simply monitoring the friends of randomly selected individuals. Such
	individuals are known to be more central. To evaluate whether such
	a friend group could indeed provide early detection, we studied a
	flu outbreak at Harvard College in late 2009. We followed 744 students
	who were either members of a group of randomly chosen individuals
	or a group of their friends. Based on clinical diagnoses, the progression
	of the epidemic in the friend group occurred 13.9 days (95\% C.I.
	9.9–16.6) in advance of the randomly chosen group (i.e., the population
	as a whole). The friend group also showed a significant lead time
	(p<0.05) on day 16 of the epidemic, a full 46 days before the peak
	in daily incidence in the population as a whole. This sensor method
	could provide significant additional time to react to epidemics in
	small or large populations under surveillance. The amount of lead
	time will depend on features of the outbreak and the network at hand.
	The method could in principle be generalized to other biological,
	psychological, informational, or behavioral contagions that spread
	in networks.}},
  citeulike-article-id = {7853548},
  citeulike-linkout-0 = {http://christakis.med.harvard.edu/pdf/publications/articles/112.pdf},
  citeulike-linkout-1 = {http://dx.doi.org/10.1371/journal.pone.0012948},
  citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/20856792},
  citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=20856792},
  day = {15},
  doi = {10.1371/journal.pone.0012948},
  issn = {1932-6203},
  pmid = {20856792},
  posted-at = {2012-03-25 16:04:15},
  priority = {2},
  publisher = {Public Library of Science},
  url = {http://christakis.med.harvard.edu/pdf/publications/articles/112.pdf}
}

@ARTICLE{Clauset2008Hierarchical,
  author = {Clauset, Aaron and Moore, Cristopher and Newman, M. E. J.},
  title = {{Hierarchical structure and the prediction of missing links in networks}},
  journal = {Nature},
  year = {2008},
  volume = {453},
  pages = {98--101},
  number = {7191},
  month = may,
  abstract = {{Networks have in recent years emerged as an invaluable tool for describing
	and quantifying complex systems in many branches of science1, 2,
	3. Recent studies suggest that networks often exhibit hierarchical
	organization, in which vertices divide into groups that further subdivide
	into groups of groups, and so forth over multiple scales. In many
	cases the groups are found to correspond to known functional units,
	such as ecological niches in food webs, modules in biochemical networks
	(protein interaction networks, metabolic networks or genetic regulatory
	networks) or communities in social networks4, 5, 6, 7. Here we present
	a general technique for inferring hierarchical structure from network
	data and show that the existence of hierarchy can simultaneously
	explain and quantitatively reproduce many commonly observed topological
	properties of networks, such as right-skewed degree distributions,
	high clustering coefficients and short path lengths. We further show
	that knowledge of hierarchical structure can be used to predict missing
	connections in partly known networks with high accuracy, and for
	more general network structures than competing techniques8. Taken
	together, our results suggest that hierarchy is a central organizing
	principle of complex networks, capable of offering insight into many
	network phenomena.}},
  citeulike-article-id = {2739852},
  citeulike-linkout-0 = {http://arxiv.org/pdf/0811.0484v1},
  citeulike-linkout-1 = {http://dx.doi.org/10.1038/nature06830},
  citeulike-linkout-2 = {http://dx.doi.org/10.1038/nature06830},
  citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/18451861},
  citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=18451861},
  day = {01},
  doi = {10.1038/nature06830},
  issn = {0028-0836},
  pmid = {18451861},
  posted-at = {2012-03-25 16:05:41},
  priority = {2},
  publisher = {Nature Publishing Group},
  url = {http://arxiv.org/pdf/0811.0484v1}
}

@ARTICLE{clauset2008hierarchical,
  author = {Clauset, A. and Moore, C. and Newman, M. E. J.},
  title = {{Hierarchical structure and the prediction of missing links in networks}},
  journal = {Nature},
  year = {2008},
  volume = {453},
  pages = {98--101},
  number = {7191},
  citeulike-article-id = {7736189},
  citeulike-linkout-0 = {http://arxiv.org/pdf/0811.0484v1},
  posted-at = {2012-03-25 15:58:16},
  priority = {2},
  url = {http://arxiv.org/pdf/0811.0484v1}
}

@ARTICLE{Dorogovtsev2001Evolution,
  author = {Dorogovtsev, S. N. and Mendes, J. F. F.},
  title = {{Evolution of networks}},
  year = {2001},
  month = sep,
  abstract = {{We review the recent fast progress in statistical physics of evolving
	networks. Interest has focused mainly on the structural properties
	of random complex networks in communications, biology, social sciences
	and economics. A number of giant artificial networks of such a kind
	came into existence recently. This opens a wide field for the study
	of their topology, evolution, and complex processes occurring in
	them. Such networks possess a rich set of scaling properties. A number
	of them are scale-free and show striking resilience against random
	breakdowns. In spite of large sizes of these networks, the distances
	between most their vertices are short -- a feature known as the ``small-world''
	effect. We discuss how growing networks self-organize into scale-free
	structures and the role of the mechanism of preferential linking.
	We consider the topological and structural properties of evolving
	networks, and percolation in these networks. We present a number
	of models demonstrating the main features of evolving networks and
	discuss current approaches for their simulation and analytical study.
	Applications of the general results to particular networks in Nature
	are discussed. We demonstrate the generic connections of the network
	growth processes with the general problems of non-equilibrium physics,
	econophysics, evolutionary biology, etc.}},
  archiveprefix = {arXiv},
  citeulike-article-id = {333030},
  citeulike-linkout-0 = {http://arxiv.org/pdf/cond-mat/0106144v2},
  citeulike-linkout-1 = {http://arxiv.org/abs/cond-mat/0106144},
  citeulike-linkout-2 = {http://arxiv.org/pdf/cond-mat/0106144},
  citeulike-linkout-3 = {http://adsabs.harvard.edu/cgi-bin/nph-bib\_query?bibcode=2001cond.mat..6144D},
  day = {7},
  eprint = {cond-mat/0106144},
  posted-at = {2012-03-25 16:08:27},
  priority = {2},
  url = {http://arxiv.org/pdf/cond-mat/0106144v2}
}

@ARTICLE{dorogovtsev00,
  author = {Dorogovtsev, S. N. and Mendes, J. F. F. and Samukhin, A. N.},
  title = {{Structure of Growing Networks with Preferential Linking}},
  journal = {Physical Review Letters},
  year = {2000},
  volume = {85},
  pages = {4633--4636},
  number = {21},
  month = nov,
  abstract = {{The model of growing networks with the preferential attachment of
	new links is generalized to include initial attractiveness of sites.
	We find the exact form of the stationary distribution of the number
	of incoming links of sites in the limit of long times, P ( q ) ,
	and the long-time limit of the average connectivity q ̅ ( s , t )
	of a site s at time t (one site is added per unit of time). At long
	times, P ( q ) ∼ q - γ at q → ∞ and q ̅ ( s , t ) ∼ ( s / t ) - β
	at s / t →0 , where the exponent γ varies from 2 to ∞ depending on
	the initial attractiveness of sites. We show that the relation β
	( γ -1)  =  1 between the exponents is universal.}},
  citeulike-article-id = {5087739},
  citeulike-linkout-0 = {http://dx.doi.org/10.1103/PhysRevLett.85.4633},
  citeulike-linkout-1 = {http://link.aps.org/abstract/PRL/v85/i21/p4633},
  citeulike-linkout-2 = {http://link.aps.org/pdf/PRL/v85/i21/p4633},
  doi = {10.1103/PhysRevLett.85.4633},
  posted-at = {2012-03-25 15:23:06},
  priority = {2},
  publisher = {American Physical Society},
  url = {http://dx.doi.org/10.1103/PhysRevLett.85.4633}
}

@ARTICLE{Goh2007Human,
  author = {Goh, Kwang-Il and Cusick, Michael E. and Valle, David and Childs,
	Barton and Vidal, Marc and Barab\'{a}si, Albert-L\'{a}szl\'{o}},
  title = {{The human disease network}},
  journal = {Proceedings of the National Academy of Sciences},
  year = {2007},
  volume = {104},
  pages = {8685--8690},
  number = {21},
  month = may,
  abstract = {{A network of disorders and disease genes linked by known disorder–gene
	associations offers a platform to explore in a single graph-theoretic
	framework all known phenotype and disease gene associations, indicating
	the common genetic origin of many diseases. Genes associated with
	similar disorders show both higher likelihood of physical interactions
	between their products and higher expression profiling similarity
	for their transcripts, supporting the existence of distinct disease-specific
	functional modules. We find that essential human genes are likely
	to encode hub proteins and are expressed widely in most tissues.
	This suggests that disease genes also would play a central role in
	the human interactome. In contrast, we find that the vast majority
	of disease genes are nonessential and show no tendency to encode
	hub proteins, and their expression pattern indicates that they are
	localized in the functional periphery of the network. A selection-based
	model explains the observed difference between essential and disease
	genes and also suggests that diseases caused by somatic mutations
	should not be peripheral, a prediction we confirm for cancer genes.}},
  citeulike-article-id = {1320727},
  citeulike-linkout-0 = {http://www.barabasilab.com/pubs/CCNR-ALB\_Publications/200705-14\_PNAS-HumanDisease/200705-14\_PNAS-HumanDisease.pdf},
  citeulike-linkout-1 = {http://dx.doi.org/10.1073/pnas.0701361104},
  citeulike-linkout-2 = {http://www.pnas.org/content/104/21/8685.full.abstract},
  citeulike-linkout-3 = {http://www.pnas.org/content/104/21/8685.full.full.pdf},
  citeulike-linkout-4 = {http://www.pnas.org/cgi/content/abstract/104/21/8685},
  citeulike-linkout-5 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1885563/},
  citeulike-linkout-6 = {http://view.ncbi.nlm.nih.gov/pubmed/17502601},
  citeulike-linkout-7 = {http://www.hubmed.org/display.cgi?uids=17502601},
  day = {22},
  doi = {10.1073/pnas.0701361104},
  issn = {0027-8424},
  pmcid = {PMC1885563},
  pmid = {17502601},
  posted-at = {2012-03-25 16:04:25},
  priority = {2},
  url = {http://www.barabasilab.com/pubs/CCNR-ALB\_Publications/200705-14\_PNAS-HumanDisease/200705-14\_PNAS-HumanDisease.pdf}
}

@ARTICLE{Goldstein2004Problems,
  author = {Goldstein, Michel L. and Morris, Steven A. and Yen, Gary G.},
  title = {{Problems with Fitting to the Power-Law Distribution}},
  year = {2004},
  month = aug,
  abstract = {{This short communication uses a simple experiment to show that fitting
	to a power law distribution by using graphical methods based on linear
	fit on the log-log scale is biased and inaccurate. It shows that
	using maximum likelihood estimation (MLE) is far more robust. Finally,
	it presents a new table for performing the Kolmogorov-Smirnof test
	for goodness-of-fit tailored to power-law distributions in which
	the power-law exponent is estimated using MLE. The techniques presented
	here will advance the application of complex network theory by allowing
	reliable estimation of power-law models from data and further allowing
	quantitative assessment of goodness-of-fit of proposed power-law
	models to empirical data.}},
  archiveprefix = {arXiv},
  citeulike-article-id = {492},
  citeulike-linkout-0 = {http://arxiv.org/pdf/cond-mat/0402322v3},
  citeulike-linkout-1 = {http://arxiv.org/abs/cond-mat/0402322},
  citeulike-linkout-2 = {http://arxiv.org/pdf/cond-mat/0402322},
  day = {13},
  eprint = {cond-mat/0402322},
  posted-at = {2012-03-25 16:08:06},
  priority = {2},
  url = {http://arxiv.org/pdf/cond-mat/0402322v3}
}

@ARTICLE{granovetter1973strength,
  author = {Granovetter, M. S.},
  title = {{The strength of weak ties}},
  journal = {American journal of sociology},
  year = {1973},
  volume = {78},
  pages = {1360},
  number = {6},
  citeulike-article-id = {7736173},
  citeulike-linkout-0 = {http://sociology.stanford.edu/people/mgranovetter/documents/granstrengthweakties.pdf},
  posted-at = {2012-03-25 15:59:37},
  priority = {2},
  publisher = {UChicago Press},
  url = {http://sociology.stanford.edu/people/mgranovetter/documents/granstrengthweakties.pdf}
}

@INPROCEEDINGS{Kempe2003Maximizing,
  author = {Kempe, David and Kleinberg, Jon and Tardos, \'{E}va},
  title = {{Maximizing the spread of influence through a social network}},
  booktitle = {Proceedings of the ninth ACM SIGKDD international conference on Knowledge
	discovery and data mining},
  year = {2003},
  series = {KDD '03},
  pages = {137--146},
  address = {New York, NY, USA},
  publisher = {ACM},
  abstract = {{Models for the processes by which ideas and influence propagate through
	a social network have been studied in a number of domains, including
	the diffusion of medical and technological innovations, the sudden
	and widespread adoption of various strategies in game-theoretic settings,
	and the effects of "word of mouth" in the promotion of new products.
	Recently, motivated by the design of viral marketing strategies,
	Domingos and Richardson posed a fundamental algorithmic problem for
	such social network processes: if we can try to convince a subset
	of individuals to adopt a new product or innovation, and the goal
	is to trigger a large cascade of further adoptions, which set of
	individuals should we target?We consider this problem in several
	of the most widely studied models in social network analysis. The
	optimization problem of selecting the most influential nodes is NP-hard
	here, and we provide the first provable approximation guarantees
	for efficient algorithms. Using an analysis framework based on submodular
	functions, we show that a natural greedy strategy obtains a solution
	that is provably within 63\% of optimal for several classes of models;
	our framework suggests a general approach for reasoning about the
	performance guarantees of algorithms for these types of influence
	problems in social networks.We also provide computational experiments
	on large collaboration networks, showing that in addition to their
	provable guarantees, our approximation algorithms significantly out-perform
	node-selection heuristics based on the well-studied notions of degree
	centrality and distance centrality from the field of social networks.}},
  citeulike-article-id = {115243},
  citeulike-linkout-0 = {http://www.cs.cornell.edu/home/kleinber/kdd03-inf.pdf},
  citeulike-linkout-1 = {http://portal.acm.org/citation.cfm?id=956769},
  citeulike-linkout-2 = {http://dx.doi.org/10.1145/956750.956769},
  doi = {10.1145/956750.956769},
  isbn = {1-58113-737-0},
  location = {Washington, D.C.},
  posted-at = {2012-03-25 16:06:51},
  priority = {2},
  url = {http://www.cs.cornell.edu/home/kleinber/kdd03-inf.pdf}
}

@MISC{Kempe2003Maximizing,
  author = {Kempe, D. and Kleinberg, J. and Tardos, E.},
  title = {{Maximizing the spread of influence through a social network}},
  year = {2003},
  abstract = {{Models for the processes by which ideas and influence propagate through
	a social network have been studied in a number of domains, including
	the diffusion of medical and technological innovations, the sudden
	and widespread adoption of various strategies in game-theoretic settings,
	and the effects of \&quot;word of mouth\&quot; in the promotion of
	new products. Recently, motivated by the design of viral marketing
	strategies, Domingos and Richardson posed a fundamental algorithmic
	problem for such social...}},
  citeulike-article-id = {416639},
  citeulike-linkout-0 = {http://www.cs.cornell.edu/home/kleinber/kdd03-inf.pdf},
  citeulike-linkout-1 = {http://citeseer.ist.psu.edu/kempe03maximizing.html},
  citeulike-linkout-2 = {http://citeseer.lcs.mit.edu/kempe03maximizing.html},
  citeulike-linkout-3 = {http://citeseer.ifi.unizh.ch/kempe03maximizing.html},
  citeulike-linkout-4 = {http://citeseer.comp.nus.edu.sg/kempe03maximizing.html},
  posted-at = {2012-03-25 16:04:03},
  priority = {2},
  url = {http://www.cs.cornell.edu/home/kleinber/kdd03-inf.pdf}
}

@ELECTRONIC{Klemm2001Growing,
  author = {Klemm, Konstantin and Eguiluz, Victor M.},
  month = jul,
  year = {2001},
  title = {{Growing Scale-Free Networks with Small World Behavior}},
  url = {http://arxiv.org/pdf/cond-mat/0107607v1},
  abstract = {{In the context of growing networks, we introduce a simple dynamical
	model that unifies the generic features of real networks: scale-free
	distribution of degree and the small world effect. While the average
	shortest path length increases logartihmically as in random networks,
	the clustering coefficient assumes a large value independent of system
	size. We derive expressions for the clustering coefficient in two
	limiting cases: random (C \~{} (ln N)^2 / N) and highly clustered
	(C = 5/6) scale-free networks.}},
  archiveprefix = {arXiv},
  citeulike-article-id = {2343142},
  citeulike-linkout-0 = {http://arxiv.org/pdf/cond-mat/0107607v1},
  citeulike-linkout-1 = {http://arxiv.org/abs/cond-mat/0107607},
  citeulike-linkout-2 = {http://arxiv.org/pdf/cond-mat/0107607},
  day = {30},
  eprint = {cond-mat/0107607},
  keywords = {network\_theory, scale-free\_networks, small\_world},
  posted-at = {2012-03-25 15:26:43},
  priority = {2}
}

@ARTICLE{kossinets2009origins,
  author = {Kossinets, G. and Watts, D. J.},
  title = {{Origins of Homophily in an Evolving Social Network 1}},
  journal = {American Journal of Sociology},
  year = {2009},
  volume = {115},
  pages = {405--450},
  number = {2},
  citeulike-article-id = {7736176},
  citeulike-linkout-0 = {http://research.yahoo.com/files/k\_w\_AJS.pdf},
  posted-at = {2012-03-25 15:59:06},
  priority = {2},
  publisher = {UChicago Press},
  url = {http://research.yahoo.com/files/k\_w\_AJS.pdf}
}

@ARTICLE{Kossinets2006Empirical,
  author = {Kossinets, Gueorgi and Watts, Duncan J.},
  title = {{Empirical Analysis of an Evolving Social Network}},
  journal = {Science},
  year = {2006},
  volume = {311},
  pages = {88--90},
  number = {5757},
  month = jan,
  abstract = {{Social networks evolve over time, driven by the shared activities
	and affiliations of their members, by similarity of individuals'
	attributes, and by the closure of short network cycles. We analyzed
	a dynamic social network comprising 43,553 students, faculty, and
	staff at a large university, in which interactions between individuals
	are inferred from time-stamped e-mail headers recorded over one academic
	year and are matched with affiliations and attributes. We found that
	network evolution is dominated by a combination of effects arising
	from network topology itself and the organizational structure in
	which the network is embedded. In the absence of global perturbations,
	average network properties appear to approach an equilibrium state,
	whereas individual properties are unstable.}},
  citeulike-article-id = {459365},
  citeulike-linkout-0 = {http://research.yahoo.com/files/k\_w\_Science.pdf},
  citeulike-linkout-1 = {http://dx.doi.org/10.1126/science.1116869},
  citeulike-linkout-2 = {http://www.sciencemag.org/content/311/5757/88.abstract},
  citeulike-linkout-3 = {http://www.sciencemag.org/content/311/5757/88.full.pdf},
  citeulike-linkout-4 = {http://www.sciencemag.org/cgi/content/abstract/311/5757/88},
  citeulike-linkout-5 = {http://view.ncbi.nlm.nih.gov/pubmed/16400149},
  citeulike-linkout-6 = {http://www.hubmed.org/display.cgi?uids=16400149},
  day = {6},
  doi = {10.1126/science.1116869},
  pmid = {16400149},
  posted-at = {2012-03-25 16:05:23},
  priority = {2},
  url = {http://research.yahoo.com/files/k\_w\_Science.pdf}
}

@INPROCEEDINGS{Kumar2006Structure,
  author = {Kumar, Ravi and Novak, Jasmine and Tomkins, Andrew},
  title = {{Structure and evolution of online social networks}},
  booktitle = {Proceedings of the 12th ACM SIGKDD international conference on Knowledge
	discovery and data mining},
  year = {2006},
  series = {KDD '06},
  pages = {611--617},
  address = {New York, NY, USA},
  publisher = {ACM},
  abstract = {{In this paper, we consider the evolution of structure within large
	online social networks. We present a series of measurements of two
	such networks, together comprising in excess of five million people
	and ten million friendship links, annotated with metadata capturing
	the time of every event in the life of the network. Our measurements
	expose a surprising segmentation of these networks into three regions:
	singletons who do not participate in the network; isolated communities
	which overwhelmingly display star structure; and a giant component
	anchored by a well-connected core region which persists even in the
	absence of stars.We present a simple model of network growth which
	captures these aspects of component structure. The model follows
	our experimental results, characterizing users as either passive
	members of the network; inviters who encourage offline friends and
	acquaintances to migrate online; and linkers who fully participate
	in the social evolution of the network.}},
  citeulike-article-id = {975331},
  citeulike-linkout-0 = {http://www.tomkinshome.com/site\_media/papers/papers/KNT06.pdf},
  citeulike-linkout-1 = {http://portal.acm.org/citation.cfm?id=1150476},
  citeulike-linkout-2 = {http://dx.doi.org/10.1145/1150402.1150476},
  doi = {10.1145/1150402.1150476},
  isbn = {1-59593-339-5},
  location = {Philadelphia, PA, USA},
  posted-at = {2012-03-25 16:06:32},
  priority = {2},
  url = {http://www.tomkinshome.com/site\_media/papers/papers/KNT06.pdf}
}

@ARTICLE{Lu2010Link,
  author = {L\"{u}, Linyuan and Zhou, Tao},
  title = {{Link prediction in complex networks: A survey}},
  journal = {Physica A: Statistical Mechanics and its Applications},
  year = {2010},
  month = dec,
  abstract = {{Link prediction in complex networks has attracted increasing attention
	from both physical and computer science communities. The algorithms
	can be used to extract missing information, identify spurious interactions,
	evaluate network evolving mechanisms, and so on. This article summaries
	recent progress about link prediction algorithms, emphasizing on
	the contributions from physical perspectives and approaches, such
	as the random-walk-based methods and the maximum likelihood methods.
	We also introduce three typical applications: reconstruction of networks,
	evaluation of network evolving mechanism and classification of partially
	labelled networks. Finally, we introduce some applications and outline
	future challenges of link prediction algorithms.}},
  citeulike-article-id = {8405125},
  citeulike-linkout-0 = {http://arxiv.org/pdf/1010.0725v1},
  citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.physa.2010.11.027},
  day = {02},
  doi = {10.1016/j.physa.2010.11.027},
  issn = {03784371},
  posted-at = {2012-03-25 16:01:55},
  priority = {2},
  url = {http://arxiv.org/pdf/1010.0725v1}
}

@INPROCEEDINGS{Leskovec2008Microscopic,
  author = {Leskovec, Jure and Backstrom, Lars and Kumar, Ravi and Tomkins, Andrew},
  title = {{Microscopic evolution of social networks}},
  booktitle = {Proceedings of the 14th ACM SIGKDD international conference on Knowledge
	discovery and data mining},
  year = {2008},
  series = {KDD '08},
  pages = {462--470},
  address = {New York, NY, USA},
  publisher = {ACM},
  abstract = {{We present a detailed study of network evolution by analyzing four
	large online social networks with full temporal information about
	node and edge arrivals. For the first time at such a large scale,
	we study individual node arrival and edge creation processes that
	collectively lead to macroscopic properties of networks. Using a
	methodology based on the maximum-likelihood principle, we investigate
	a wide variety of network formation strategies, and show that edge
	locality plays a critical role in evolution of networks. Our findings
	supplement earlier network models based on the inherently non-local
	preferential attachment. Based on our observations, we develop a
	complete model of network evolution, where nodes arrive at a prespecified
	rate and select their lifetimes. Each node then independently initiates
	edges according to a "gap" process, selecting a destination for each
	edge according to a simple triangle-closing model free of any parameters.
	We show analytically that the combination of the gap distribution
	with the node lifetime leads to a power law out-degree distribution
	that accurately reflects the true network in all four cases. Finally,
	we give model parameter settings that allow automatic evolution and
	generation of realistic synthetic networks of arbitrary scale.}},
  citeulike-article-id = {3352998},
  citeulike-linkout-0 = {http://cs.stanford.edu/people/jure/pubs/microEvol-kdd08.pdf},
  citeulike-linkout-1 = {http://portal.acm.org/citation.cfm?id=1401890.1401948},
  citeulike-linkout-2 = {http://dx.doi.org/10.1145/1401890.1401948},
  doi = {10.1145/1401890.1401948},
  isbn = {978-1-60558-193-4},
  location = {Las Vegas, Nevada, USA},
  posted-at = {2012-03-25 16:08:33},
  priority = {2},
  url = {http://cs.stanford.edu/people/jure/pubs/microEvol-kdd08.pdf}
}

@INPROCEEDINGS{leskovec2008microscopic,
  author = {Leskovec, J. and Backstrom, L. and Kumar, R. and Tomkins, A.},
  title = {{Microscopic evolution of social networks}},
  booktitle = {Proceeding of the 14th ACM SIGKDD international conference on Knowledge
	discovery and data mining},
  year = {2008},
  pages = {462--470},
  organization = {ACM},
  citeulike-article-id = {7736197},
  citeulike-linkout-0 = {http://cs.stanford.edu/people/jure/pubs/microEvol-kdd08.pdf},
  posted-at = {2012-03-25 15:58:03},
  priority = {2},
  url = {http://cs.stanford.edu/people/jure/pubs/microEvol-kdd08.pdf}
}

@ARTICLE{leskovec2007graph,
  author = {Leskovec, J. and Kleinberg, J. and Faloutsos, C.},
  title = {{Graph evolution: Densification and shrinking diameters}},
  journal = {ACM Transactions on Knowledge Discovery from Data (TKDD)},
  year = {2007},
  volume = {1},
  pages = {2},
  number = {1},
  citeulike-article-id = {7736198},
  citeulike-linkout-0 = {http://cs.stanford.edu/people/jure/pubs/powergrowth-tkdd.pdf},
  posted-at = {2012-03-25 15:57:52},
  priority = {2},
  publisher = {ACM},
  url = {http://cs.stanford.edu/people/jure/pubs/powergrowth-tkdd.pdf}
}

@ELECTRONIC{LibenNowell2004Link,
  author = {Liben-Nowell, David and Kleinberg, Jon},
  year = {2004},
  title = {{The Link Prediction Problem for Social Networks}},
  url = {http://www.cs.cornell.edu/home/kleinber/link-pred.pdf},
  abstract = {{Given a snapshot of a social network, can we infer which new interactions
	among its members are likely to occur in the near future? We formalize
	this question as the link prediction problem, and develop approaches
	to link prediction based on measures for analyzing the \&\#034;proximity\&\#034;
	of nodes in a network. Experiments on large co-authorship networks
	suggest that information about future interactions can be extracted
	from network topology alone, and that fairly subtle measures for
	detecting node proximity can outperform more direct measures.}},
  citeulike-article-id = {7926514},
  citeulike-linkout-0 = {http://www.cs.cornell.edu/home/kleinber/link-pred.pdf},
  citeulike-linkout-1 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.8378},
  posted-at = {2012-03-25 16:02:34},
  priority = {2}
}

@ARTICLE{Marsili2004Rise,
  author = {Marsili, Matteo and Vega-Redondo, Fernando and Slanina, Franti\v{s}ek},
  title = {{The rise and fall of a networked society: A formal model}},
  journal = {Proceedings of the National Academy of Sciences of the United States
	of America},
  year = {2004},
  volume = {101},
  pages = {1439--1442},
  number = {6},
  month = feb,
  abstract = {{In a well networked community, there is intense social interaction,
	and information disseminates briskly and broadly. This is important
	if the environment is volatile (i.e., keeps changing) and individuals
	never stop searching for fresh opportunities. Here, we present a
	simple model that attributes the rise of a dynamic society to the
	emergence of some key features in its social network. We also explain
	the apparently paradoxical observation that although such features
	do not necessarily materialize even under favorable conditions they
	display a significant resilience to deteriorating conditions. We
	interpret these findings as a discontinuous phase transition in the
	network formation process.}},
  address = {The Abdus Salam International Centre for Theoretical Physics, Strada
	Costiera 11, 34014 Trieste, Italy.},
  citeulike-article-id = {112851},
  citeulike-linkout-0 = {http://arxiv.org/pdf/physics/0505019.pdf},
  citeulike-linkout-1 = {http://dx.doi.org/10.1073/pnas.0305684101},
  citeulike-linkout-2 = {http://www.pnas.org/content/101/6/1439.abstract},
  citeulike-linkout-3 = {http://www.pnas.org/content/101/6/1439.full.pdf},
  citeulike-linkout-4 = {http://www.pnas.org/cgi/content/abstract/101/6/1439},
  citeulike-linkout-5 = {http://view.ncbi.nlm.nih.gov/pubmed/14745030},
  citeulike-linkout-6 = {http://www.hubmed.org/display.cgi?uids=14745030},
  day = {10},
  doi = {10.1073/pnas.0305684101},
  issn = {0027-8424},
  pmid = {14745030},
  posted-at = {2012-03-25 15:29:44},
  priority = {2},
  url = {http://arxiv.org/pdf/physics/0505019.pdf}
}

@MISC{MitzenmacherBrief,
  author = {Mitzenmacher, M.},
  title = {{A brief history of generative models for power law and lognormal
	distributions}},
  abstract = {{Recently, I became interested in a current debate over whether file
	size distributions are best modelled by a power law distribution
	or a a lognormal distribution.}},
  citeulike-article-id = {227279},
  citeulike-linkout-0 = {http://www.eecs.harvard.edu/\~{}michaelm/postscripts/im2004a.pdf},
  citeulike-linkout-1 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.6305},
  posted-at = {2012-03-25 16:01:18},
  priority = {2},
  url = {http://www.eecs.harvard.edu/\~{}michaelm/postscripts/im2004a.pdf}
}

@ELECTRONIC{Newman2001Clustering,
  author = {Newman, M. E. J.},
  month = apr,
  year = {2001},
  title = {{Clustering and preferential attachment in growing networks}},
  url = {http://arxiv.org/pdf/cond-mat/0104209v1},
  abstract = {{We study empirically the time evolution of scientific collaboration
	networks in physics and biology. In these networks, two scientists
	are considered connected if they have coauthored one or more papers
	together. We show that the probability of scientists collaborating
	increases with the number of other collaborators they have in common,
	and that the probability of a particular scientist acquiring new
	collaborators increases with the number of his or her past collaborators.
	These results provide experimental evidence in favor of previously
	conjectured mechanisms for clustering and power-law degree distributions
	in networks.}},
  archiveprefix = {arXiv},
  citeulike-article-id = {150391},
  citeulike-linkout-0 = {http://arxiv.org/pdf/cond-mat/0104209v1},
  citeulike-linkout-1 = {http://arxiv.org/abs/cond-mat/0104209},
  citeulike-linkout-2 = {http://arxiv.org/pdf/cond-mat/0104209},
  day = {11},
  eprint = {cond-mat/0104209},
  posted-at = {2012-03-25 16:01:53},
  priority = {2}
}

@ELECTRONIC{Newman2001Egocentered,
  author = {Newman, M. E. J.},
  month = nov,
  year = {2001},
  title = {{Ego-centered networks and the ripple effect}},
  url = {http://arxiv.org/pdf/cond-mat/0111070v1},
  abstract = {{Recent work has demonstrated that many social networks, and indeed
	many networks of other types also, have broad distributions of vertex
	degree. Here we show that this has a substantial impact on the shape
	of ego-centered networks, i.e., sets of network vertices that are
	within a given distance of a specified central vertex, the ego. This
	in turn affects concepts and methods based on ego-centered networks,
	such as snowball sampling and the "ripple effect". In particular,
	we argue that one's acquaintances, one's immediate neighbors in the
	acquaintance network, are far from being a random sample of the population,
	and that this biases the numbers of neighbors two and more steps
	away. We demonstrate this concept using data drawn from academic
	collaboration networks, for which, as we show, current simple theories
	for the typical size of ego-centered networks give numbers that differ
	greatly from those measured in reality. We present an improved theoretical
	model which gives significantly better results.}},
  archiveprefix = {arXiv},
  citeulike-article-id = {219665},
  citeulike-linkout-0 = {http://arxiv.org/pdf/cond-mat/0111070v1},
  citeulike-linkout-1 = {http://arxiv.org/abs/cond-mat/0111070},
  citeulike-linkout-2 = {http://arxiv.org/pdf/cond-mat/0111070},
  day = {5},
  eprint = {cond-mat/0111070},
  posted-at = {2012-03-25 15:57:10},
  priority = {2}
}

@ARTICLE{Newman2003Why,
  author = {Newman, M. E. J. and Park, Juyong},
  title = {{Why social networks are different from other types of networks}},
  journal = {Physical Review E},
  year = {2003},
  volume = {68},
  pages = {036122+},
  number = {3},
  month = sep,
  abstract = {{We argue that social networks differ from most other types of networks,
	including technological and biological networks, in two important
	ways. First, they have nontrivial clustering or network transitivity
	and second, they show positive correlations, also called assortative
	mixing, between the degrees of adjacent vertices. Social networks
	are often divided into groups or communities, and it has recently
	been suggested that this division could account for the observed
	clustering. We demonstrate that group structure in networks can also
	account for degree correlations. We show using a simple model that
	we should expect assortative mixing in such networks whenever there
	is variation in the sizes of the groups and that the predicted level
	of assortative mixing compares well with that observed in real-world
	networks.}},
  archiveprefix = {arXiv},
  citeulike-article-id = {336118},
  citeulike-linkout-0 = {http://arxiv.org/pdf/cond-mat/0305612v1},
  citeulike-linkout-1 = {http://arxiv.org/abs/cond-mat/0305612},
  citeulike-linkout-2 = {http://arxiv.org/pdf/cond-mat/0305612},
  citeulike-linkout-3 = {http://dx.doi.org/10.1103/PhysRevE.68.036122},
  citeulike-linkout-4 = {http://link.aps.org/abstract/PRE/v68/i3/e036122},
  citeulike-linkout-5 = {http://link.aps.org/pdf/PRE/v68/i3/e036122},
  day = {26},
  doi = {10.1103/PhysRevE.68.036122},
  eprint = {cond-mat/0305612},
  posted-at = {2012-03-25 16:04:59},
  priority = {2},
  publisher = {American Physical Society},
  url = {http://arxiv.org/pdf/cond-mat/0305612v1}
}

@INPROCEEDINGS{Nowell2003Link,
  author = {Nowell, David L. and Kleinberg, Jon},
  title = {{The link prediction problem for social networks}},
  booktitle = {Proceedings of the twelfth international conference on Information
	and knowledge management},
  year = {2003},
  series = {CIKM '03},
  pages = {556--559},
  address = {New York, NY, USA},
  publisher = {ACM},
  abstract = {{Given a snapshot of a social network, can we infer which new interactions
	among its members are likely to occur in the near future? We formalize
	this question as the link prediction problem, and develop approaches
	to link prediction based on measures the "proximity" of nodes in
	a network. Experiments on large co-authorship networks suggest that
	information about future interactions can be extracted from network
	topology alone, and that fairly subtle measures for detecting node
	proximity can outperform more direct measures.}},
  citeulike-article-id = {595771},
  citeulike-linkout-0 = {http://www.cs.cornell.edu/home/kleinber/link-pred.pdf},
  citeulike-linkout-1 = {http://portal.acm.org/citation.cfm?id=956972},
  citeulike-linkout-2 = {http://dx.doi.org/10.1145/956863.956972},
  doi = {10.1145/956863.956972},
  isbn = {1-58113-723-0},
  location = {New Orleans, LA, USA},
  posted-at = {2012-03-25 16:06:43},
  priority = {2},
  url = {http://www.cs.cornell.edu/home/kleinber/link-pred.pdf}
}

@ELECTRONIC{Ravasz2002Hierarchical,
  author = {Ravasz, Erzsebet and Barabasi, Albert-Laszlo},
  month = sep,
  year = {2002},
  title = {{Hierarchical Organization in Complex Networks}},
  url = {http://www.barabasilab.com/pubs/CCNR-ALB\_Publications/200302-14\_PhysRevE-HierarchicalOrg/200302-14\_PhysRevE-HierarchicalOrg.pdf},
  abstract = {{Many real networks in nature and society share two generic properties:
	they are scale-free and they display a high degree of clustering.
	We show that these two features are the consequence of a hierarchical
	organization, implying that small groups of nodes organize in a hierarchical
	manner into increasingly large groups, while maintaining a scale-free
	topology. In hierarchical networks the degree of clustering characterizing
	the different groups follows a strict scaling law, which can be used
	to identify the presence of a hierarchical organization in real networks.
	We find that several real networks, such as the World Wide Web, actor
	network, the Internet at the domain level and the semantic web obey
	this scaling law, indicating that hierarchy is a fundamental characteristic
	of many complex systems.}},
  archiveprefix = {arXiv},
  citeulike-article-id = {341229},
  citeulike-linkout-0 = {http://www.barabasilab.com/pubs/CCNR-ALB\_Publications/200302-14\_PhysRevE-HierarchicalOrg/200302-14\_PhysRevE-HierarchicalOrg.pdf},
  citeulike-linkout-1 = {http://arxiv.org/abs/cond-mat/0206130},
  citeulike-linkout-2 = {http://arxiv.org/pdf/cond-mat/0206130},
  day = {1},
  eprint = {cond-mat/0206130},
  posted-at = {2012-03-25 16:04:38},
  priority = {2}
}

@MISC{vespignani01,
  author = {Satorras, Pastor R. and Vespignani, A.},
  title = {{Epidemic spreading in scale-free networks}},
  year = {2001},
  abstract = {{The Internet, as well as many other networks, has a very complex
	connectivity
	
	recently modeled by the class of scale-free networks. This feature,
	which
	
	appears to be very efficient for a communications network, favors
	at the same
	
	time the spreading of computer viruses. We analyze real data from
	computer
	
	virus infections and find the average lifetime and prevalence of viral
	strains on
	
	the Internet. We define a dynamical model for the spreading of infections
	on
	
	scale-free networks, finding...}},
  citeulike-article-id = {1103577},
  citeulike-linkout-0 = {http://xxx.lanl.gov/pdf/cond-mat/0010317v1.pdf},
  citeulike-linkout-1 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.7011},
  posted-at = {2012-03-25 15:22:37},
  priority = {2},
  url = {http://xxx.lanl.gov/pdf/cond-mat/0010317v1.pdf}
}

@INPROCEEDINGS{seshadri2008mobile,
  author = {Seshadri, M. and Machiraju, S. and Sridharan, A. and Bolot, J. and
	Faloutsos, C. and Leskove, J.},
  title = {{Mobile call graphs: beyond power-law and lognormal distributions}},
  booktitle = {Proceeding of the 14th ACM SIGKDD international conference on Knowledge
	discovery and data mining},
  year = {2008},
  pages = {596--604},
  organization = {ACM},
  citeulike-article-id = {7736181},
  citeulike-linkout-0 = {http://cs.stanford.edu/people/jure/pubs/dpln-kdd08.pdf},
  posted-at = {2012-03-25 15:58:40},
  priority = {2},
  url = {http://cs.stanford.edu/people/jure/pubs/dpln-kdd08.pdf}
}

@ARTICLE{toivonen08,
  author = {Toivonen, Riitta and Kovanen, Lauri and Kivel\&\#xe4;, Mikko and
	Onnela, Jukka-Pekka and Saram\&\#xe4;ki, Jari and Kaski, Kimmo},
  title = {{A comparative study of social network models: network evolution
	models and nodal attribute models}},
  year = {2008},
  month = dec,
  abstract = {{This paper reviews, classifies and compares recent models for social
	networks that have mainly been published within the physics-oriented
	complex networks literature. The models fall into two categories:
	those in which the addition of new links is dependent on the (typically
	local) network structure (network evolution models, NEMs), and those
	in which links are generated based only on nodal attributes (nodal
	attribute models, NAMs). An exponential random graph model (ERGM)
	with structural dependencies is included for comparison. We fit models
	from each of these categories to two empirical acquaintance networks
	with respect to basic network properties. We compare higher order
	structures in the resulting networks with those in the data, with
	the aim of determining which models produce the most realistic network
	structure with respect to degree distributions, assortativity, clustering
	spectra, geodesic path distributions, and community structure (subgroups
	with dense internal connections). We find that the nodal attribute
	models successfully produce assortative networks and very clear community
	structure. However, they generate unrealistic clustering spectra
	and peaked degree distributions that do not match empirical data
	on large social networks. On the other hand, many of the network
	evolution models produce degree distributions and clustering spectra
	that agree more closely with data. They also generate assortative
	networks and community structure, although often not to the same
	extent as in the data. The ERG model turns out to produce the weakest
	community structure.}},
  archiveprefix = {arXiv},
  citeulike-article-id = {3887585},
  citeulike-linkout-0 = {http://jponnela.com/web\_documents/a22.pdf},
  citeulike-linkout-1 = {http://arxiv.org/abs/0805.0512},
  citeulike-linkout-2 = {http://arxiv.org/pdf/0805.0512},
  day = {24},
  eprint = {0805.0512},
  keywords = {network\_theory, social\_networks},
  posted-at = {2012-03-25 15:26:25},
  priority = {2},
  url = {http://jponnela.com/web\_documents/a22.pdf}
}

@ARTICLE{Volz2004Random,
  author = {Volz, E.},
  title = {{Random networks with tunable degree distribution and clustering.}},
  journal = {Phys Rev E Stat Nonlin Soft Matter Phys},
  year = {2004},
  volume = {70},
  number = {5 Pt 2},
  month = nov,
  abstract = {{We present an algorithm for generating random networks with arbitrary
	degree distribution and clustering (frequency of triadic closure).
	We use this algorithm to generate networks with exponential, power
	law, and Poisson degree distributions with variable levels of clustering.
	Such networks may be used as models of social networks and as a testable
	null hypothesis about network structure. Finally, we explore the
	effects of clustering on the point of the phase transition where
	a giant component forms in a random network, and on the size of the
	giant component. Some analysis of these effects is presented.}},
  address = {Cornell University, Ithaca, New York 14853, USA. emv7@cornell.edu},
  citeulike-article-id = {99833},
  citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/15600700},
  citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=15600700},
  issn = {1539-3755},
  pmid = {15600700},
  posted-at = {2012-03-25 15:27:18},
  priority = {2},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/15600700}
}

@INPROCEEDINGS{White2003Algorithms,
  author = {White, Scott and Smyth, Padhraic},
  title = {{Algorithms for estimating relative importance in networks}},
  booktitle = {KDD '03: Proceedings of the ninth ACM SIGKDD international conference
	on Knowledge discovery and data mining},
  year = {2003},
  pages = {266--275},
  address = {New York, NY, USA},
  publisher = {ACM},
  abstract = {{Large and complex graphs representing relationships among sets of
	entities are an increasingly common focus of interest in data analysis---examples
	include social networks, Web graphs, telecommunication networks,
	and biological networks. In interactive analysis of such data a natural
	query is "which entities are most important in the network relative
	to a particular individual or set of individuals?" We investigate
	the problem of answering such queries in this paper, focusing in
	particular on defining and computing the importance of nodes in a
	graph relative to one or more root nodes. We define a general framework
	and a number of different algorithms, building on ideas from social
	networks, graph theory, Markov models, and Web graph analysis. We
	experimentally evaluate the different properties of these algorithms
	on toy graphs and demonstrate how our approach can be used to study
	relative importance in real-world networks including a network of
	interactions among September 11th terrorists, a network of collaborative
	research in biotechnology among companies and universities, and a
	network of co-authorship relationships among computer science researchers.}},
  citeulike-article-id = {142464},
  citeulike-linkout-0 = {http://datamining.dongguk.ac.kr/graph/p266-white.pdf},
  citeulike-linkout-1 = {http://portal.acm.org/citation.cfm?id=956782},
  citeulike-linkout-2 = {http://dx.doi.org/10.1145/956750.956782},
  doi = {10.1145/956750.956782},
  isbn = {1-58113-737-0},
  location = {Washington, D.C.},
  posted-at = {2012-03-25 16:08:13},
  priority = {2},
  url = {http://datamining.dongguk.ac.kr/graph/p266-white.pdf}
}

